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Abstract #2162

qMTNet+: artificial neural network with residual connection for accelerated quantitative magnetization transfer imaging

Huan Minh Luu1, Dong-Hyun Kim1, Seung-Hong Choi2, and Sung-Hong Park1
1Magnetic Resonance Imaging Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

Quantitative magnetization transfer (qMT) imaging provides quantitative measures of magnetization transfer properties, but the method itself suffers from long acquisition and processing time. Previous research has looked into the application of deep learning to accelerate qMT imaging. Specifically, a network called qMTNet was proposed to accelerate both data acquisition and fitting. In this study, we propose qMTNet+, an improved version of qMTNet, that accomplishes both acceleration tasks as well as generation of missing data with a single residual network. Results showed that qMTNet+ improves the quality of generated MT images and fitted qMT parameters compared to qMTNet.

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